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train_SB3.py
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from __future__ import annotations
import os
import json
from environment.gymnasium_envs.trimesh_full_env import TriMeshEnvFull
import mesh_model.random_trimesh as TM
from plots.mesh_plotter import dataset_plt
from exploit_SB3_policy import testPolicy
from stable_baselines3 import PPO,SAC
from stable_baselines3.common.env_checker import check_env
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.logger import Figure
import gymnasium as gym
class TensorboardCallback(BaseCallback):
"""
Custom callback for plotting additional values in tensorboard.
"""
def __init__(self, model, verbose=0):
super().__init__(verbose)
self.model = model
self.episode_rewards = []
self.mesh_reward = 0
self.current_episode_reward = 0
self.episode_count = 0
self.current_episode_length = 0
self.actions_info = {
"episode_valid_actions": 0,
"episode_invalid_topo": 0,
"episode_invalid_geo": 0,
"nb_flip" : 0,
"nb_split": 0,
"nb_collapse": 0,
"nb_invalid_flip": 0,
"nb_invalid_split": 0,
"nb_invalid_collapse": 0,
}
self.final_distance = 0
self.normalized_return = 0
def _on_training_start(self) -> None:
"""
Record PPO parameters and environment configuration at the training start.
"""
self.logger.record("parameters/ppo", f"<pre>{json.dumps(ppo_config, indent=4)}</pre>")
self.logger.record("parameters/env", f"<pre>{json.dumps(env_config, indent=4)}</pre>")
self.logger.dump(step=0)
def _on_step(self) -> bool:
"""
Record different learning variables to monitor
"""
self.current_episode_reward += self.locals["rewards"][0]
self.current_episode_length += 1
self.actions_info["episode_valid_actions"] += self.locals["infos"][0].get("valid_action", 0.0)
self.actions_info["episode_invalid_topo"] += self.locals["infos"][0].get("invalid_topo", 0.0)
self.actions_info["episode_invalid_geo"] += self.locals["infos"][0].get("invalid_geo", 0.0)
self.actions_info["nb_flip"] += self.locals["infos"][0].get("flip", 0.0)
self.actions_info["nb_split"] += self.locals["infos"][0].get("split", 0.0)
self.actions_info["nb_collapse"] += self.locals["infos"][0].get("collapse", 0.0)
self.actions_info["nb_invalid_flip"] += self.locals["infos"][0].get("invalid_flip", 0.0)
self.actions_info["nb_invalid_split"] += self.locals["infos"][0].get("invalid_split", 0.0)
self.actions_info["nb_invalid_collapse"] += self.locals["infos"][0].get("invalid_collapse", 0.0)
self.mesh_reward += self.locals["infos"][0].get("mesh_reward", 0.0)
# When the episode is over
if self.locals["dones"][0]:
self.episode_rewards.append(self.current_episode_reward) # global rewards obtained during the episode
mesh_ideal_reward = self.locals["infos"][0].get("mesh_ideal_rewards", 0.0) # maximum achievable reward
if mesh_ideal_reward > 0:
self.normalized_return = self.mesh_reward/ mesh_ideal_reward
else:
self.normalized_return = 0
self.final_distance = self.locals["infos"][0].get("distance", 0.0)
self.logger.record("final_distance", self.final_distance)
self.logger.record("valid_actions", self.actions_info["episode_valid_actions"]*100/self.current_episode_length if self.current_episode_length > 0 else 0)
self.logger.record("n_invalid_topo", self.actions_info["episode_invalid_topo"])
self.logger.record("n_invalid_geo", self.actions_info["episode_invalid_geo"])
self.logger.record("nb_flip", self.actions_info["nb_flip"])
self.logger.record("nb_split", self.actions_info["nb_split"])
self.logger.record("nb_collapse", self.actions_info["nb_collapse"])
self.logger.record("invalid_flip", self.actions_info["nb_invalid_flip"]*100/self.actions_info["nb_flip"] if self.actions_info["nb_flip"] > 0 else 0)
self.logger.record("invalid_split", self.actions_info["nb_invalid_split"]*100/self.actions_info["nb_split"] if self.actions_info["nb_split"] > 0 else 0)
self.logger.record("invalid_collapse", self.actions_info["nb_invalid_collapse"]*100/self.actions_info["nb_collapse"]if self.actions_info["nb_collapse"] > 0 else 0)
self.logger.record("episode_mesh_reward", self.mesh_reward)
self.logger.record("episode_reward", self.current_episode_reward)
self.logger.record("normalized_return", self.normalized_return)
self.logger.record("episode_length", self.current_episode_length)
is_success = self.locals["infos"][0].get("is_success", 0.0) # Default value: 0.0
self.logger.record("episode_success", is_success)
self.logger.dump(step=self.episode_count)
self.current_episode_reward = 0 # resets global episode reward
self.mesh_reward = 0 # resets mesh episode reward
self.current_episode_length = 0
#reset actions info
for key in self.actions_info.keys():
self.actions_info[key] = 0
self.episode_count += 1 # Increment episode counter
return True
def _on_training_end(self) -> None:
"""
Records policy evaluation results : before and after dataset images
"""
dataset = [TM.random_mesh(30) for _ in range(9)] # dataset of 9 meshes of size 30
before = dataset_plt(dataset) # plot the datasat as image
length, wins, rewards, normalized_return, final_meshes = testPolicy(self.model, 10, env_config, dataset) # test model policy on the dataset
after = dataset_plt(final_meshes)
self.logger.record("figures/before", Figure(before, close=True), exclude=("stdout", "log"))
self.logger.record("figures/after", Figure(after, close=True), exclude=("stdout", "log"))
self.logger.dump(step=0)
with open("model_RL/parameters/ppo_config.json", "r") as f:
ppo_config = json.load(f)
with open("environment/parameters/environment_config.json", "r") as f:
env_config = json.load(f)
# Create log dir
log_dir = ppo_config["tensorboard_log"]
os.makedirs(log_dir, exist_ok=True)
# Create the environment
env = gym.make(
env_config["env_name"],
mesh_size=env_config["mesh_size"],
max_episode_steps=env_config["max_episode_steps"],
n_darts_selected=env_config["n_darts_selected"],
deep= env_config["deep"],
action_restriction=env_config["action_restriction"],
with_degree_obs=env_config["with_degree_observation"]
)
check_env(env, warn=True)
model = PPO(
policy=ppo_config["policy"],
env=env,
n_steps=ppo_config["n_steps"],
n_epochs=ppo_config["n_epochs"],
batch_size=ppo_config["batch_size"],
learning_rate=ppo_config["learning_rate"],
gamma=ppo_config["gamma"],
verbose=ppo_config["verbose"],
tensorboard_log=log_dir
)
print("-----------Starting learning-----------")
model.learn(total_timesteps=ppo_config["total_timesteps"], callback=TensorboardCallback(model))
print("-----------Learning ended------------")
model.save("final-2")